STPNet: Quantifying the Uncertainty of Electric Vehicle Charging Demand via Long-Term Spatiotemporal Traffic Flow Prediction Intervals

Constructing the prediction intervals (PIs) for electric vehicle (EV) charging demand based on traffic flow information is crucial for the efficient operation of EV charging stations. However, due to the volatile nature of traffic flow, obtaining long-term traffic flow information (e.g., one week in advance) is challenging, particularly for multiple neighbored sites. To address this issue, this work establishes a deep learning prediction framework called Spatiotemporal Periodic Network (STPNet), which utilizes an encoder-decoder architecture. The STPNet incorporates a spatiotemporal and periodic pattern learning technique, and leverages the advantages of convolutional long short-term memory units (ConvLSTM) to quantify the uncertainty of traffic flow. Furthermore, to improve the performance of traffic flow prediction, a spatiotemporal series decomposition strategy based on Seasonal and Trend decomposition using Loess (STL) is employed, and a spatiotemporal PI performance-based loss function is creatively developed in this work. Then, the PIs of the EV charging demand are obtained based on the predicted traffic flow information and an M/M/C/K queuing model. Validated using a real-world dataset, the proposed model has been demonstrated to exhibit effectiveness in generating high-quality EV charging demand PIs for multiple locations.